Overview

Dataset statistics

Number of variables18
Number of observations59354
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.2 MiB
Average record size in memory144.0 B

Variable types

Numeric9
Categorical9

Warnings

Price is highly correlated with AreaHigh correlation
Number of rooms is highly correlated with AreaHigh correlation
Area is highly correlated with Price and 1 other fieldsHigh correlation
Garden Area is highly correlated with Surface of the landHigh correlation
Surface of the land is highly correlated with Garden AreaHigh correlation
Price is highly correlated with Area and 1 other fieldsHigh correlation
Number of rooms is highly correlated with Area and 1 other fieldsHigh correlation
Area is highly correlated with Price and 2 other fieldsHigh correlation
Garden Area is highly correlated with Surface of the landHigh correlation
Surface of the land is highly correlated with Price and 3 other fieldsHigh correlation
Number of rooms is highly correlated with Area and 1 other fieldsHigh correlation
Area is highly correlated with Number of rooms and 1 other fieldsHigh correlation
Surface of the land is highly correlated with Number of rooms and 1 other fieldsHigh correlation
Unnamed: 0 is highly correlated with Type of propertyHigh correlation
Surface of the land is highly correlated with Garden AreaHigh correlation
Locality is highly correlated with Province and 1 other fieldsHigh correlation
Area is highly correlated with Number of roomsHigh correlation
Number of rooms is highly correlated with AreaHigh correlation
Garden Area is highly correlated with Surface of the landHigh correlation
Province is highly correlated with Locality and 1 other fieldsHigh correlation
Region is highly correlated with Locality and 1 other fieldsHigh correlation
Type of property is highly correlated with Unnamed: 0High correlation
Province is highly correlated with RegionHigh correlation
Region is highly correlated with ProvinceHigh correlation
Number of rooms is highly skewed (γ1 = 22.35695618) Skewed
Terrace Area is highly skewed (γ1 = 83.44226517) Skewed
Garden Area is highly skewed (γ1 = 187.3438513) Skewed
Surface of the land is highly skewed (γ1 = 185.9877999) Skewed
PriceperMeter is highly skewed (γ1 = 113.6810042) Skewed
Unnamed: 0 has unique values Unique
Number of rooms has 685 (1.2%) zeros Zeros
Terrace Area has 37451 (63.1%) zeros Zeros
Garden Area has 49295 (83.1%) zeros Zeros

Reproduction

Analysis started2021-05-31 14:08:56.479474
Analysis finished2021-05-31 14:09:08.670319
Duration12.19 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct59354
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29897.7879
Minimum0
Maximum59615
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size463.8 KiB
2021-05-31T16:09:08.720322image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2967.65
Q115100.25
median29938.5
Q344776.75
95-th percentile56647.35
Maximum59615
Range59615
Interquartile range (IQR)29676.5

Descriptive statistics

Standard deviation17193.88567
Coefficient of variation (CV)0.5750888904
Kurtosis-1.194182304
Mean29897.7879
Median Absolute Deviation (MAD)14838.5
Skewness-0.008322199244
Sum1774553303
Variance295629704.4
MonotonicityStrictly increasing
2021-05-31T16:09:08.813767image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
382001
 
< 0.1%
341061
 
< 0.1%
361551
 
< 0.1%
463961
 
< 0.1%
484451
 
< 0.1%
423021
 
< 0.1%
443511
 
< 0.1%
218561
 
< 0.1%
239051
 
< 0.1%
Other values (59344)59344
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
596151
< 0.1%
596141
< 0.1%
596131
< 0.1%
596121
< 0.1%
596111
< 0.1%
596101
< 0.1%
596091
< 0.1%
596081
< 0.1%
596071
< 0.1%
596061
< 0.1%

Locality
Real number (ℝ≥0)

HIGH CORRELATION

Distinct972
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5303.593288
Minimum1000
Maximum9991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size463.8 KiB
2021-05-31T16:09:08.912200image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1060
Q12200
median4960
Q38430
95-th percentile9340
Maximum9991
Range8991
Interquartile range (IQR)6230

Descriptive statistics

Standard deviation3075.732929
Coefficient of variation (CV)0.579933785
Kurtosis-1.595137598
Mean5303.593288
Median Absolute Deviation (MAD)3260
Skewness-0.02947427814
Sum314789476
Variance9460133.052
MonotonicityNot monotonic
2021-05-31T16:09:09.001862image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10501131
 
1.9%
84001119
 
1.9%
11801082
 
1.8%
90001070
 
1.8%
83001065
 
1.8%
2000975
 
1.6%
1000877
 
1.5%
4000812
 
1.4%
8000665
 
1.1%
8370658
 
1.1%
Other values (962)49900
84.1%
ValueCountFrequency (%)
1000877
1.5%
1020110
 
0.2%
1030319
 
0.5%
1040370
 
0.6%
10501131
1.9%
1060185
 
0.3%
1070433
 
0.7%
1080266
 
0.4%
1081105
 
0.2%
108275
 
0.1%
ValueCountFrequency (%)
999160
0.1%
999046
0.1%
99884
 
< 0.1%
99821
 
< 0.1%
99804
 
< 0.1%
997115
 
< 0.1%
99703
 
< 0.1%
99689
 
< 0.1%
99611
 
< 0.1%
99606
 
< 0.1%

Type of property
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size463.8 KiB
apartment
33341 
house
26013 

Length

Max length9
Median length9
Mean length7.246925228
Min length5

Characters and Unicode

Total characters430134
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowapartment
2nd rowapartment
3rd rowapartment
4th rowapartment
5th rowapartment

Common Values

ValueCountFrequency (%)
apartment33341
56.2%
house26013
43.8%

Length

2021-05-31T16:09:09.160121image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-31T16:09:09.212804image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
apartment33341
56.2%
house26013
43.8%

Most occurring characters

ValueCountFrequency (%)
a66682
15.5%
t66682
15.5%
e59354
13.8%
p33341
7.8%
r33341
7.8%
m33341
7.8%
n33341
7.8%
h26013
 
6.0%
o26013
 
6.0%
u26013
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter430134
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a66682
15.5%
t66682
15.5%
e59354
13.8%
p33341
7.8%
r33341
7.8%
m33341
7.8%
n33341
7.8%
h26013
 
6.0%
o26013
 
6.0%
u26013
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Latin430134
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a66682
15.5%
t66682
15.5%
e59354
13.8%
p33341
7.8%
r33341
7.8%
m33341
7.8%
n33341
7.8%
h26013
 
6.0%
o26013
 
6.0%
u26013
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII430134
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a66682
15.5%
t66682
15.5%
e59354
13.8%
p33341
7.8%
r33341
7.8%
m33341
7.8%
n33341
7.8%
h26013
 
6.0%
o26013
 
6.0%
u26013
 
6.0%

Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct7344
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean370636.9388
Minimum33500
Maximum9876543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size463.8 KiB
2021-05-31T16:09:09.273407image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum33500
5-th percentile126999.65
Q1209999
median276658
Q3385000
95-th percentile894999
Maximum9876543
Range9843043
Interquartile range (IQR)175001

Descriptive statistics

Standard deviation382064.1726
Coefficient of variation (CV)1.030831341
Kurtosis59.8741589
Mean370636.9388
Median Absolute Deviation (MAD)81658
Skewness6.068615237
Sum2.199878487 × 1010
Variance1.45973032 × 1011
MonotonicityNot monotonic
2021-05-31T16:09:09.371966image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
249000620
 
1.0%
299000591
 
1.0%
295000554
 
0.9%
225000525
 
0.9%
275000486
 
0.8%
199000485
 
0.8%
235000479
 
0.8%
265000419
 
0.7%
325000418
 
0.7%
285000413
 
0.7%
Other values (7334)54364
91.6%
ValueCountFrequency (%)
335001
 
< 0.1%
340001
 
< 0.1%
349993
 
< 0.1%
350004
< 0.1%
370001
 
< 0.1%
379991
 
< 0.1%
390002
 
< 0.1%
399001
 
< 0.1%
399994
< 0.1%
400009
< 0.1%
ValueCountFrequency (%)
98765431
 
< 0.1%
95000001
 
< 0.1%
85000001
 
< 0.1%
65000002
 
< 0.1%
64999991
 
< 0.1%
63999991
 
< 0.1%
59500005
< 0.1%
59499993
< 0.1%
58499991
 
< 0.1%
54499991
 
< 0.1%

Number of rooms
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.686120565
Minimum0
Maximum165
Zeros685
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size463.8 KiB
2021-05-31T16:09:09.458203image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2.6
Q33
95-th percentile5
Maximum165
Range165
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.554511204
Coefficient of variation (CV)0.5787198177
Kurtosis2049.588682
Mean2.686120565
Median Absolute Deviation (MAD)0.6
Skewness22.35695618
Sum159432
Variance2.416505082
MonotonicityNot monotonic
2021-05-31T16:09:09.536137image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
221823
36.8%
318316
30.9%
17016
 
11.8%
46664
 
11.2%
52473
 
4.2%
61000
 
1.7%
0685
 
1.2%
2.6590
 
1.0%
7319
 
0.5%
8207
 
0.3%
Other values (22)261
 
0.4%
ValueCountFrequency (%)
0685
 
1.2%
17016
 
11.8%
221823
36.8%
2.6590
 
1.0%
318316
30.9%
46664
 
11.2%
52473
 
4.2%
61000
 
1.7%
7319
 
0.5%
8207
 
0.3%
ValueCountFrequency (%)
1651
 
< 0.1%
501
 
< 0.1%
401
 
< 0.1%
371
 
< 0.1%
342
< 0.1%
321
 
< 0.1%
303
< 0.1%
253
< 0.1%
243
< 0.1%
221
 
< 0.1%

Area
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct771
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.5463153
Minimum1
Maximum11366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size463.8 KiB
2021-05-31T16:09:09.792577image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile55
Q188
median119
Q3170
95-th percentile344
Maximum11366
Range11365
Interquartile range (IQR)82

Descriptive statistics

Standard deviation130.3195885
Coefficient of variation (CV)0.8714329617
Kurtosis1022.147654
Mean149.5463153
Median Absolute Deviation (MAD)37
Skewness16.55504415
Sum8876172
Variance16983.19514
MonotonicityNot monotonic
2021-05-31T16:09:09.884559image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001201
 
2.0%
901136
 
1.9%
1201017
 
1.7%
1101003
 
1.7%
80983
 
1.7%
150927
 
1.6%
85901
 
1.5%
140866
 
1.5%
130836
 
1.4%
95833
 
1.4%
Other values (761)49651
83.7%
ValueCountFrequency (%)
12
 
< 0.1%
51
 
< 0.1%
81
 
< 0.1%
101
 
< 0.1%
121
 
< 0.1%
133
 
< 0.1%
141
 
< 0.1%
1516
< 0.1%
1630
0.1%
1726
< 0.1%
ValueCountFrequency (%)
113661
 
< 0.1%
40003
< 0.1%
39891
 
< 0.1%
36211
 
< 0.1%
36001
 
< 0.1%
28801
 
< 0.1%
26001
 
< 0.1%
25004
< 0.1%
20001
 
< 0.1%
19252
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size463.8 KiB
0.0
51127 
1.0
8227 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters178062
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.051127
86.1%
1.08227
 
13.9%

Length

2021-05-31T16:09:10.025982image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-31T16:09:10.069946image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.051127
86.1%
1.08227
 
13.9%

Most occurring characters

ValueCountFrequency (%)
0110481
62.0%
.59354
33.3%
18227
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number118708
66.7%
Other Punctuation59354
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110481
93.1%
18227
 
6.9%
Other Punctuation
ValueCountFrequency (%)
.59354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common178062
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110481
62.0%
.59354
33.3%
18227
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII178062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110481
62.0%
.59354
33.3%
18227
 
4.6%

Furnished
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size463.8 KiB
0.0
57208 
1.0
 
2146

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters178062
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057208
96.4%
1.02146
 
3.6%

Length

2021-05-31T16:09:10.180264image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-31T16:09:10.226664image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.057208
96.4%
1.02146
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0116562
65.5%
.59354
33.3%
12146
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number118708
66.7%
Other Punctuation59354
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116562
98.2%
12146
 
1.8%
Other Punctuation
ValueCountFrequency (%)
.59354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common178062
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116562
65.5%
.59354
33.3%
12146
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII178062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116562
65.5%
.59354
33.3%
12146
 
1.2%

Open fire
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size463.8 KiB
0.0
57017 
1.0
 
2337

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters178062
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.057017
96.1%
1.02337
 
3.9%

Length

2021-05-31T16:09:10.337236image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-31T16:09:10.381993image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.057017
96.1%
1.02337
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0116371
65.4%
.59354
33.3%
12337
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number118708
66.7%
Other Punctuation59354
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0116371
98.0%
12337
 
2.0%
Other Punctuation
ValueCountFrequency (%)
.59354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common178062
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0116371
65.4%
.59354
33.3%
12337
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII178062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0116371
65.4%
.59354
33.3%
12337
 
1.3%

Terrace Area
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct304
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.63459918
Minimum0
Maximum9636
Zeros37451
Zeros (%)63.1%
Negative0
Negative (%)0.0%
Memory size463.8 KiB
2021-05-31T16:09:10.445089image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311
95-th percentile44.35
Maximum9636
Range9636
Interquartile range (IQR)11

Descriptive statistics

Standard deviation62.09448707
Coefficient of variation (CV)5.838911838
Kurtosis10898.802
Mean10.63459918
Median Absolute Deviation (MAD)0
Skewness83.44226517
Sum631206
Variance3855.725325
MonotonicityNot monotonic
2021-05-31T16:09:10.541190image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037451
63.1%
101402
 
2.4%
201077
 
1.8%
121040
 
1.8%
151028
 
1.7%
81001
 
1.7%
6906
 
1.5%
9896
 
1.5%
14722
 
1.2%
7694
 
1.2%
Other values (294)13137
 
22.1%
ValueCountFrequency (%)
037451
63.1%
181
 
0.1%
2316
 
0.5%
3365
 
0.6%
4622
 
1.0%
5618
 
1.0%
6906
 
1.5%
7694
 
1.2%
81001
 
1.7%
9896
 
1.5%
ValueCountFrequency (%)
96361
 
< 0.1%
45471
 
< 0.1%
40001
 
< 0.1%
38001
 
< 0.1%
30001
 
< 0.1%
19581
 
< 0.1%
18901
 
< 0.1%
16131
 
< 0.1%
15003
< 0.1%
14631
 
< 0.1%

Garden Area
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1163
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.9400714
Minimum0
Maximum950002
Zeros49295
Zeros (%)83.1%
Negative0
Negative (%)0.0%
Memory size463.8 KiB
2021-05-31T16:09:10.637366image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile366.7
Maximum950002
Range950002
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4291.863315
Coefficient of variation (CV)29.01082359
Kurtosis40617.19413
Mean147.9400714
Median Absolute Deviation (MAD)0
Skewness187.3438513
Sum8780835
Variance18420090.71
MonotonicityNot monotonic
2021-05-31T16:09:10.729095image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
049295
83.1%
100294
 
0.5%
200252
 
0.4%
50232
 
0.4%
300201
 
0.3%
150188
 
0.3%
500163
 
0.3%
40154
 
0.3%
60152
 
0.3%
30151
 
0.3%
Other values (1153)8272
 
13.9%
ValueCountFrequency (%)
049295
83.1%
1108
 
0.2%
21
 
< 0.1%
36
 
< 0.1%
48
 
< 0.1%
56
 
< 0.1%
613
 
< 0.1%
714
 
< 0.1%
818
 
< 0.1%
917
 
< 0.1%
ValueCountFrequency (%)
9500021
 
< 0.1%
2350001
 
< 0.1%
1500001
 
< 0.1%
1000001
 
< 0.1%
809781
 
< 0.1%
800003
< 0.1%
750001
 
< 0.1%
650002
< 0.1%
545151
 
< 0.1%
540001
 
< 0.1%

Surface of the land
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2065
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308.1209859
Minimum1
Maximum950852
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size463.8 KiB
2021-05-31T16:09:10.821766image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile58
Q195
median135
Q3213
95-th percentile668
Maximum950852
Range950851
Interquartile range (IQR)118

Descriptive statistics

Standard deviation4305.753659
Coefficient of variation (CV)13.97423043
Kurtosis40213.19274
Mean308.1209859
Median Absolute Deviation (MAD)48
Skewness185.9877999
Sum18288213
Variance18539514.57
MonotonicityNot monotonic
2021-05-31T16:09:10.911762image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100858
 
1.4%
90770
 
1.3%
120736
 
1.2%
110723
 
1.2%
80691
 
1.2%
150665
 
1.1%
140655
 
1.1%
130648
 
1.1%
105634
 
1.1%
95612
 
1.0%
Other values (2055)52362
88.2%
ValueCountFrequency (%)
11
 
< 0.1%
51
 
< 0.1%
81
 
< 0.1%
101
 
< 0.1%
121
 
< 0.1%
132
 
< 0.1%
141
 
< 0.1%
1515
< 0.1%
1625
< 0.1%
1725
< 0.1%
ValueCountFrequency (%)
9508521
< 0.1%
2352401
< 0.1%
1513401
< 0.1%
1002201
< 0.1%
832281
< 0.1%
802451
< 0.1%
802161
< 0.1%
801951
< 0.1%
758301
< 0.1%
665001
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size463.8 KiB
2.0
44306 
4.0
7672 
3.0
7015 
1.0
 
361

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters178062
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
2.044306
74.6%
4.07672
 
12.9%
3.07015
 
11.8%
1.0361
 
0.6%

Length

2021-05-31T16:09:11.055237image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-31T16:09:11.100230image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.044306
74.6%
4.07672
 
12.9%
3.07015
 
11.8%
1.0361
 
0.6%

Most occurring characters

ValueCountFrequency (%)
.59354
33.3%
059354
33.3%
244306
24.9%
47672
 
4.3%
37015
 
3.9%
1361
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number118708
66.7%
Other Punctuation59354
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
059354
50.0%
244306
37.3%
47672
 
6.5%
37015
 
5.9%
1361
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.59354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common178062
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.59354
33.3%
059354
33.3%
244306
24.9%
47672
 
4.3%
37015
 
3.9%
1361
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII178062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.59354
33.3%
059354
33.3%
244306
24.9%
47672
 
4.3%
37015
 
3.9%
1361
 
0.2%

Swimming pool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size463.8 KiB
0.0
58381 
1.0
 
973

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters178062
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.058381
98.4%
1.0973
 
1.6%

Length

2021-05-31T16:09:11.220942image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-31T16:09:11.265559image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.058381
98.4%
1.0973
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0117735
66.1%
.59354
33.3%
1973
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number118708
66.7%
Other Punctuation59354
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0117735
99.2%
1973
 
0.8%
Other Punctuation
ValueCountFrequency (%)
.59354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common178062
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0117735
66.1%
.59354
33.3%
1973
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII178062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0117735
66.1%
.59354
33.3%
1973
 
0.5%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size463.8 KiB
good
27056 
medium
25699 
to renovate
5349 
new
 
1250

Length

Max length11
Median length6
Mean length5.475738788
Min length3

Characters and Unicode

Total characters325007
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmedium
2nd rowmedium
3rd rowmedium
4th rowmedium
5th rowmedium

Common Values

ValueCountFrequency (%)
good27056
45.6%
medium25699
43.3%
to renovate5349
 
9.0%
new1250
 
2.1%

Length

2021-05-31T16:09:11.395470image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-31T16:09:11.450338image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
good27056
41.8%
medium25699
39.7%
renovate5349
 
8.3%
to5349
 
8.3%
new1250
 
1.9%

Most occurring characters

ValueCountFrequency (%)
o64810
19.9%
d52755
16.2%
m51398
15.8%
e37647
11.6%
g27056
8.3%
i25699
 
7.9%
u25699
 
7.9%
t10698
 
3.3%
n6599
 
2.0%
5349
 
1.6%
Other values (4)17297
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter319658
98.4%
Space Separator5349
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o64810
20.3%
d52755
16.5%
m51398
16.1%
e37647
11.8%
g27056
8.5%
i25699
 
8.0%
u25699
 
8.0%
t10698
 
3.3%
n6599
 
2.1%
r5349
 
1.7%
Other values (3)11948
 
3.7%
Space Separator
ValueCountFrequency (%)
5349
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin319658
98.4%
Common5349
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o64810
20.3%
d52755
16.5%
m51398
16.1%
e37647
11.8%
g27056
8.5%
i25699
 
8.0%
u25699
 
8.0%
t10698
 
3.3%
n6599
 
2.1%
r5349
 
1.7%
Other values (3)11948
 
3.7%
Common
ValueCountFrequency (%)
5349
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII325007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o64810
19.9%
d52755
16.2%
m51398
15.8%
e37647
11.6%
g27056
8.3%
i25699
 
7.9%
u25699
 
7.9%
t10698
 
3.3%
n6599
 
2.0%
5349
 
1.6%
Other values (4)17297
 
5.3%

Province
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size463.8 KiB
Flandre Occidental
12626 
Flandre Oriental
7352 
Hainaut
7096 
Brussel
6982 
Anvers
6903 
Other values (6)
18395 

Length

Max length18
Median length8
Mean length11.14202918
Min length5

Characters and Unicode

Total characters661324
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnvers
2nd rowBrabant Flamand
3rd rowFlandre Occidental
4th rowAnvers
5th rowAnvers

Common Values

ValueCountFrequency (%)
Flandre Occidental12626
21.3%
Flandre Oriental7352
12.4%
Hainaut7096
12.0%
Brussel6982
11.8%
Anvers6903
11.6%
Liège5652
9.5%
Brabant Flamand4592
 
7.7%
Brabant Wallon2838
 
4.8%
Limbourg2756
 
4.6%
Namur1606
 
2.7%

Length

2021-05-31T16:09:11.589204image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
flandre19978
23.0%
occidental12626
14.6%
brabant7430
 
8.6%
oriental7352
 
8.5%
hainaut7096
 
8.2%
brussel6982
 
8.0%
anvers6903
 
8.0%
liège5652
 
6.5%
flamand4592
 
5.3%
wallon2838
 
3.3%
Other values (3)5313
 
6.1%

Most occurring characters

ValueCountFrequency (%)
a82636
12.5%
n68815
 
10.4%
e60444
 
9.1%
l57206
 
8.7%
r53958
 
8.2%
d37196
 
5.6%
i35482
 
5.4%
t34504
 
5.2%
27408
 
4.1%
c25252
 
3.8%
Other values (17)178423
27.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter547154
82.7%
Uppercase Letter86762
 
13.1%
Space Separator27408
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a82636
15.1%
n68815
12.6%
e60444
11.0%
l57206
10.5%
r53958
9.9%
d37196
6.8%
i35482
6.5%
t34504
6.3%
c25252
 
4.6%
s20867
 
3.8%
Other values (8)70794
12.9%
Uppercase Letter
ValueCountFrequency (%)
F24570
28.3%
O19978
23.0%
B14412
16.6%
L9359
 
10.8%
H7096
 
8.2%
A6903
 
8.0%
W2838
 
3.3%
N1606
 
1.9%
Space Separator
ValueCountFrequency (%)
27408
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin633916
95.9%
Common27408
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a82636
13.0%
n68815
10.9%
e60444
 
9.5%
l57206
 
9.0%
r53958
 
8.5%
d37196
 
5.9%
i35482
 
5.6%
t34504
 
5.4%
c25252
 
4.0%
F24570
 
3.9%
Other values (16)153853
24.3%
Common
ValueCountFrequency (%)
27408
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII655672
99.1%
Latin 1 Sup5652
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a82636
12.6%
n68815
 
10.5%
e60444
 
9.2%
l57206
 
8.7%
r53958
 
8.2%
d37196
 
5.7%
i35482
 
5.4%
t34504
 
5.3%
27408
 
4.2%
c25252
 
3.9%
Other values (16)172771
26.4%
Latin 1 Sup
ValueCountFrequency (%)
è5652
100.0%

Region
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size463.8 KiB
Flanders
34229 
Wallonia
18143 
Brussel
6982 

Length

Max length8
Median length8
Mean length7.882366816
Min length7

Characters and Unicode

Total characters467850
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFlanders
2nd rowFlanders
3rd rowFlanders
4th rowFlanders
5th rowFlanders

Common Values

ValueCountFrequency (%)
Flanders34229
57.7%
Wallonia18143
30.6%
Brussel6982
 
11.8%

Length

2021-05-31T16:09:11.743055image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-31T16:09:11.791144image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
flanders34229
57.7%
wallonia18143
30.6%
brussel6982
 
11.8%

Most occurring characters

ValueCountFrequency (%)
l77497
16.6%
a70515
15.1%
n52372
11.2%
s48193
10.3%
e41211
8.8%
r41211
8.8%
F34229
7.3%
d34229
7.3%
W18143
 
3.9%
o18143
 
3.9%
Other values (3)32107
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter408496
87.3%
Uppercase Letter59354
 
12.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l77497
19.0%
a70515
17.3%
n52372
12.8%
s48193
11.8%
e41211
10.1%
r41211
10.1%
d34229
8.4%
o18143
 
4.4%
i18143
 
4.4%
u6982
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
F34229
57.7%
W18143
30.6%
B6982
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
Latin467850
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l77497
16.6%
a70515
15.1%
n52372
11.2%
s48193
10.3%
e41211
8.8%
r41211
8.8%
F34229
7.3%
d34229
7.3%
W18143
 
3.9%
o18143
 
3.9%
Other values (3)32107
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII467850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l77497
16.6%
a70515
15.1%
n52372
11.2%
s48193
10.3%
e41211
8.8%
r41211
8.8%
F34229
7.3%
d34229
7.3%
W18143
 
3.9%
o18143
 
3.9%
Other values (3)32107
6.9%

PriceperMeter
Real number (ℝ≥0)

SKEWED

Distinct5852
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2709.809903
Minimum109
Maximum562499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size463.8 KiB
2021-05-31T16:09:11.855974image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum109
5-th percentile1000
Q11804
median2441
Q33149
95-th percentile5132
Maximum562499
Range562390
Interquartile range (IQR)1345

Descriptive statistics

Standard deviation3121.018553
Coefficient of variation (CV)1.151748154
Kurtosis18820.27894
Mean2709.809903
Median Absolute Deviation (MAD)668
Skewness113.6810042
Sum160838057
Variance9740756.808
MonotonicityNot monotonic
2021-05-31T16:09:11.945631image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2500243
 
0.4%
3000169
 
0.3%
2000131
 
0.2%
1666122
 
0.2%
2499113
 
0.2%
1500108
 
0.2%
2333100
 
0.2%
214286
 
0.1%
225086
 
0.1%
260079
 
0.1%
Other values (5842)58117
97.9%
ValueCountFrequency (%)
1091
< 0.1%
1131
< 0.1%
1662
< 0.1%
1671
< 0.1%
1891
< 0.1%
1901
< 0.1%
2111
< 0.1%
2141
< 0.1%
2201
< 0.1%
2301
< 0.1%
ValueCountFrequency (%)
5624991
< 0.1%
2949991
< 0.1%
1430001
< 0.1%
999991
< 0.1%
485361
< 0.1%
412491
< 0.1%
373121
< 0.1%
371421
< 0.1%
349991
< 0.1%
308132
< 0.1%

Interactions

2021-05-31T16:09:01.300509image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:01.386553image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:01.469278image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:01.553861image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:01.640616image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:01.730045image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:01.815953image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:01.899641image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:01.982919image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:02.066508image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:02.146965image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:02.222715image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:02.300056image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:02.385030image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:02.466137image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:02.551944image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:02.630197image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:02.707652image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:02.787476image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:02.870970image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:02.949011image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:03.031543image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:03.228296image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:03.307780image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:03.386755image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:03.464486image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:03.545191image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:03.626864image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:03.710744image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:03.793339image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:03.881882image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:03.969349image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:04.050911image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:04.131746image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:04.212813image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:04.296995image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:04.382688image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:04.471491image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:04.550344image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:04.638520image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:04.722839image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:04.803353image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:04.884464image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:04.963540image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:05.041524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:05.122977image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:05.205776image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:05.289956image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:05.371097image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:05.453578image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:05.534474image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:05.613376image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:05.691147image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:05.772181image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:05.854400image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:05.934879image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:06.013526image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:06.091675image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:06.305952image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:06.384073image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:06.464472image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:06.542231image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:06.619359image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:06.696416image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:06.775378image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:06.853415image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:06.934658image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:07.014643image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:07.092341image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:07.170440image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:07.248510image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:07.325906image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:07.403589image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:07.487441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:07.566240image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:07.644740image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:07.727691image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:07.809411image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:07.890072image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:07.968527image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-31T16:09:08.051526image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-05-31T16:09:12.037441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-31T16:09:12.175400image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-31T16:09:12.319618image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-31T16:09:12.458301image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-31T16:09:12.593190image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-31T16:09:08.220001image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-31T16:09:08.480118image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0LocalityType of propertyPriceNumber of roomsAreaFully equipped kitchenFurnishedOpen fireTerrace AreaGarden AreaSurface of the landNumber of facadesSwimming poolState of the buildingProvinceRegionPriceperMeter
002970apartment764999.02.0153.00.00.00.062.00.0215.02.00.0mediumAnversFlanders4999.0
113200apartment294999.02.080.00.00.00.00.00.080.02.00.0mediumBrabant FlamandFlanders3687.0
228211apartment233999.02.090.00.00.00.00.00.090.02.00.0mediumFlandre OccidentalFlanders2599.0
332630apartment329899.01.087.00.00.00.028.00.0115.02.00.0mediumAnversFlanders3791.0
442630apartment359899.01.095.00.00.00.047.00.0142.04.00.0mediumAnversFlanders3788.0
554432apartment248999.03.0125.00.00.00.00.00.0125.02.00.0mediumLiègeWallonia1991.0
664432apartment412299.02.0125.00.00.00.00.00.0125.04.00.0mediumLiègeWallonia3298.0
779300apartment144999.01.070.00.00.00.00.00.070.02.00.0mediumFlandre OrientalFlanders2071.0
889300apartment238999.01.047.00.00.00.00.00.047.02.00.0mediumFlandre OrientalFlanders5085.0
999300apartment129999.01.067.00.00.00.00.00.067.02.00.0mediumFlandre OrientalFlanders1940.0

Last rows

Unnamed: 0LocalityType of propertyPriceNumber of roomsAreaFully equipped kitchenFurnishedOpen fireTerrace AreaGarden AreaSurface of the landNumber of facadesSwimming poolState of the buildingProvinceRegionPriceperMeter
59344596064600house511376.03.0203.00.00.00.049.00.0252.04.00.0mediumLiègeWallonia2519.0
59345596078610house257211.03.0134.00.00.00.00.00.0134.03.00.0mediumFlandre OccidentalFlanders1919.0
59346596087880house332200.03.0170.00.00.00.00.00.0170.04.00.0mediumHainautWallonia1954.0
59347596097880house334900.03.0165.00.00.00.00.00.0165.04.00.0mediumHainautWallonia2029.0
59348596107880house340500.03.0167.00.00.00.00.00.0167.04.00.0mediumHainautWallonia2038.0
59349596118902house307242.03.0150.00.00.00.00.00.0150.03.00.0mediumFlandre OccidentalFlanders2048.0
59350596129600house315000.03.0150.00.00.00.00.00.0150.03.00.0goodFlandre OrientalFlanders2100.0
59351596139600house315000.03.0150.00.00.00.00.00.0150.03.00.0goodFlandre OrientalFlanders2100.0
59352596146000house175000.04.0205.00.00.00.023.0600.0828.02.00.0mediumHainautWallonia853.0
59353596156000house185000.04.0200.00.00.00.023.0800.01023.03.00.0mediumHainautWallonia925.0